{"id":"https://openalex.org/W4393305319","doi":"https://doi.org/10.1109/tetci.2024.3378652","title":"Effective Single-Step Adversarial Training With Energy-Based Models","display_name":"Effective Single-Step Adversarial Training With Energy-Based Models","publication_year":2024,"publication_date":"2024-03-29","ids":{"openalex":"https://openalex.org/W4393305319","doi":"https://doi.org/10.1109/tetci.2024.3378652"},"language":"en","primary_location":{"id":"doi:10.1109/tetci.2024.3378652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2024.3378652","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047533842","display_name":"Keke Tang","orcid":"https://orcid.org/0000-0003-0377-1022"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keke Tang","raw_affiliation_strings":["Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0377-1022","affiliations":[{"raw_affiliation_string":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005165749","display_name":"Tianrui Lou","orcid":"https://orcid.org/0000-0002-5325-3445"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrui Lou","raw_affiliation_strings":["School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-5325-3445","affiliations":[{"raw_affiliation_string":"School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052138365","display_name":"Weilong Peng","orcid":"https://orcid.org/0000-0001-5820-889X"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weilong Peng","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5820-889X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016232773","display_name":"Nenglun Chen","orcid":"https://orcid.org/0000-0002-3104-3017"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nenglun Chen","raw_affiliation_strings":["School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3104-3017","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066351987","display_name":"Yawen Shi","orcid":"https://orcid.org/0000-0002-3580-0652"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yawen Shi","raw_affiliation_strings":["Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3580-0652","affiliations":[{"raw_affiliation_string":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100668416","display_name":"Wenping Wang","orcid":"https://orcid.org/0000-0002-2284-3952"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenping Wang","raw_affiliation_strings":["Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-2284-3952","affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0547,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.92091477,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"8","issue":"5","first_page":"3396","last_page":"3407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9797999858856201,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.9081965088844299},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6282294392585754},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6019420623779297},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47950345277786255},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.47510847449302673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35800689458847046},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1461787223815918},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0781756043434143}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9081965088844299},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6282294392585754},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6019420623779297},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47950345277786255},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.47510847449302673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35800689458847046},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1461787223815918},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0781756043434143},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetci.2024.3378652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2024.3378652","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8100000023841858}],"awards":[{"id":"https://openalex.org/G1177137375","display_name":null,"funder_award_id":"62102105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2318274618","display_name":null,"funder_award_id":"2022A1515011501","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G8075237089","display_name":null,"funder_award_id":"2022A1515010138","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2133453031","https://openalex.org/W2194775991","https://openalex.org/W2521028896","https://openalex.org/W2620038827","https://openalex.org/W2621006664","https://openalex.org/W2746600820","https://openalex.org/W2774644650","https://openalex.org/W2919115771","https://openalex.org/W2963140844","https://openalex.org/W2963178695","https://openalex.org/W2963446712","https://openalex.org/W2964137095","https://openalex.org/W2964335397","https://openalex.org/W2967593122","https://openalex.org/W2997532515","https://openalex.org/W3004127093","https://openalex.org/W3034842981","https://openalex.org/W3035005405","https://openalex.org/W3089774673","https://openalex.org/W3090787324","https://openalex.org/W3096488581","https://openalex.org/W3135970545","https://openalex.org/W3196302005","https://openalex.org/W4226110825","https://openalex.org/W4226117697","https://openalex.org/W4290935136","https://openalex.org/W4293846201","https://openalex.org/W4312545227","https://openalex.org/W4382466349","https://openalex.org/W4385654548","https://openalex.org/W4387846635","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6640425456","https://openalex.org/W6683825394","https://openalex.org/W6712574313","https://openalex.org/W6729756640","https://openalex.org/W6745272055","https://openalex.org/W6746608116","https://openalex.org/W6747819456","https://openalex.org/W6748475379","https://openalex.org/W6759129252","https://openalex.org/W6761100157","https://openalex.org/W6762775584","https://openalex.org/W6771809012","https://openalex.org/W6774469542","https://openalex.org/W6780176858","https://openalex.org/W6784323503","https://openalex.org/W6786179718","https://openalex.org/W6787972765","https://openalex.org/W6801832226","https://openalex.org/W6803971153","https://openalex.org/W6810115246"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Adversarial":[0],"training":[1,90],"(AT)":[2],"is":[3,16,21],"one":[4],"of":[5,45,65,69,92,107,118],"the":[6,42,52,66,89,105,116,132],"most":[7],"effective":[8,36],"ways":[9],"against":[10],"adversarial":[11,94,122],"attacks.":[12],"However,":[13],"multi-step":[14,38],"AT":[15,20,34],"time-consuming":[17],"while":[18],"single-step":[19,33],"ineffective.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26,50,79],"propose":[27],"an":[28,81],"Energy-AT":[29,114],"framework":[30],"to":[31,58,62,87,104,121],"make":[32],"as":[35,37],"ones,":[39],"by":[40],"exploiting":[41],"two":[43],"properties":[44],"energy-based":[46],"models":[47,108,119],"(EBM).":[48],"First,":[49],"utilize":[51],"Helmholtz":[53],"free":[54],"energy":[55],"in":[56,85,124],"EBM":[57,86],"push":[59],"generated":[60],"examples":[61,95,102],"be":[63],"outside":[64],"distribution":[67],"boundaries":[68],"their":[70],"categories,":[71],"such":[72,97],"that":[73,98,113],"they":[74],"are":[75],"more":[76],"adversarial.":[77],"Second,":[78],"apply":[80],"adaptive":[82],"temperature":[83],"scheme":[84],"amplify":[88],"gradients":[91],"weak":[93],"targetedly,":[96],"those":[99],"originally":[100],"hard-to-learn":[101],"contribute":[103],"robustification":[106],"also.":[109],"Extensive":[110],"experiments":[111],"validate":[112],"improves":[115],"robustness":[117],"significantly":[120],"attacks":[123],"both":[125],"white-box":[126],"and":[127,130],"black-box":[128],"settings,":[129],"outperforms":[131],"state-of-the-art":[133],"methods.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}